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Vibrational resonance in adaptive small-world neuronal networks with spike-timing-dependent plasticity

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  • Yu, Haitao
  • Guo, Xinmeng
  • Wang, Jiang
  • Deng, Bin
  • Wei, Xile

Abstract

The phenomenon of vibrational resonance is investigated in adaptive Newman–Watts small-world neuronal networks, where the strength of synaptic connections between neurons is modulated based on spike-timing-dependent plasticity. Numerical results demonstrate that there exists appropriate amplitude of high-frequency driving which is able to optimize the neural ensemble response to the weak low-frequency periodic signal. The effect of networked vibrational resonance can be significantly affected by spike-timing-dependent plasticity. It is shown that spike-timing-dependent plasticity with dominant depression can always improve the efficiency of vibrational resonance, and a small adjusting rate can promote the transmission of weak external signal in small-world neuronal networks. In addition, the network topology plays an important role in the vibrational resonance in spike-timing-dependent plasticity-induced neural systems, where the system response to the subthreshold signal is maximized by an optimal network structure. Furthermore, it is demonstrated that the introduction of inhibitory synapses can considerably weaken the phenomenon of vibrational resonance in the hybrid small-world neuronal networks with spike-timing-dependent plasticity.

Suggested Citation

  • Yu, Haitao & Guo, Xinmeng & Wang, Jiang & Deng, Bin & Wei, Xile, 2015. "Vibrational resonance in adaptive small-world neuronal networks with spike-timing-dependent plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 170-179.
    • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:170-179
    DOI: 10.1016/j.physa.2015.05.037
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    References listed on IDEAS

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    1. Ming Xue & Jiang Wang & Bin Deng & Xile Wei, 2013. "Vibrational resonance in feedforward neuronal network with unreliable synapses," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(4), pages 1-9, April.
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    Cited by:

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    3. Liu, Huixia & Lu, Lulu & Zhu, Yuan & Wei, Zhouchao & Yi, Ming, 2022. "Stochastic resonance: The response to envelope modulation signal for neural networks with different topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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